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Implementation of a Scenario-based MPC for HVAC Systems: an Experimental Case Study
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0001-8633-1641
Technical University of Luleå.
KTH, School of Electrical Engineering (EES), Automatic Control.ORCID iD: 0000-0002-2300-2581
KTH, School of Electrical Engineering (EES), Automatic Control.
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2014 (English)In: Proceedings of the 19th IFAC World Congress, 2014, Elsevier BV , 2014, Vol. 47, p. 599-605Conference paper, Published paper (Refereed)
Abstract [en]

Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and air quality levels. Model Predictive Control (MPC) techniques are known to bring significant energy savings potential. Developing effective MPC-based control strategies for HVAC systems is nontrivial since buildings dynamics are nonlinear and influenced by various uncertainties. This complicates the use of MPC techniques in practice. We propose to address this issue by designing a stochastic MPC strategy that dynamically learns the statistics of the building occupancy patterns and weather conditions. The main advantage of this method is the absence of a-priori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the practical implementation of the proposed MPC controller on a student laboratory, showing its effectiveness and computational tractability.

Place, publisher, year, edition, pages
Elsevier BV , 2014. Vol. 47, p. 599-605
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-165779DOI: 10.3182/20140824-6-ZA-1003.02629Scopus ID: 2-s2.0-84929740458ISBN: 978-3-902823-62-5 (print)OAI: oai:DiVA.org:kth-165779DiVA, id: diva2:808797
Conference
19th IFAC World Congress, 2014-08-24 - 2014-08-29 Cape Town, South Africa
Note

QC 20150430. QC 20200710

Available from: 2015-04-29 Created: 2015-04-29 Last updated: 2024-03-18Bibliographically approved

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Parisio, AlessandraMolinari, MarcoJohansson, Karl Henrik

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Parisio, AlessandraVaragnolo, DamiannoMolinari, MarcoPattarello, GiorgioFabietti, LucaJohansson, Karl Henrik
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CiteExportLink to record
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Citation style
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